AI Strategy • Enterprise Data Architecture • GenAI Platforms • Cloud Modernization • Team Leadership
I lead at the intersection of AI, data, cloud, engineering, and business impact.
Turning complex enterprise problems into scalable data products, AI-powered workflows, executive dashboards, and measurable outcomes.
I am a Senior Manager and AI/Data leader with nearly 12 years of experience across enterprise data platforms, financial services analytics, GenAI adoption, cloud modernization, and consulting-led delivery.
My strength is the blend of managerial ownership + technical depth: I can work with leadership on strategy, partner with stakeholders on outcomes, and go deep with engineering teams on architecture, data pipelines, AI systems, governance, and delivery execution.
- 🧭 Leadership: AI & data strategy, delivery ownership, stakeholder management, roadmap planning, team enablement
- 🤖 GenAI: Claude, OpenAI/Codex, LLM apps, RAG, agentic workflows, prompt engineering, AI-assisted SDLC
- ☁️ Cloud: AWS, Azure, GCP, cloud data platforms, modernization, scalable architecture, automation
- 📊 Data: Data engineering, analytics, BI, dashboards, data quality, governance, SQL/Python, ML workflows
- 🏦 Domain: Financial services, risk-aware analytics, enterprise reporting, business transformation
- 🎓 Certifications: Strong certification portfolio across AI, data, cloud, analytics, and enterprise technology
My operating principle: AI should not be a demo. It should become a trusted, governed, measurable capability inside the business.
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AI, Data & Cloud Transformation
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EPAM / Consulting-led Delivery
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Morgan Stanley / BFSI Analytics
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Many enterprise/client projects cannot be fully public on GitHub, so this profile highlights my leadership direction, technical toolkit, and selected personal/open projects.
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leadership_identity:
- Senior Manager: AI, Data, Cloud and Business Transformation
- Strategy + Architecture + Delivery + Governance
- Enterprise AI adoption with measurable business outcomes
ai_now:
- Claude for analysis, reasoning, research and business workflows
- OpenAI Codex for AI-assisted software delivery and automation
- RAG systems over enterprise knowledge and documents
- Agentic AI for operations, analytics, support and engineering productivity
- LLM evaluation, safety, governance and observability
data_now:
- Data products and data platform modernization
- Cloud-native data engineering on AWS, Azure and GCP
- Executive analytics, BI modernization and KPI frameworks
- ML/AI pipelines that are reliable, explainable and scalable
managerial_edge:
- Translating strategy into delivery roadmaps
- Aligning engineering, product, risk and business stakeholders
- Building teams that can ship, scale and sustain AI/data capabilities
I maintain a certification-heavy learning profile across the areas that matter most for modern AI/data leadership.
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flowchart LR
A[Business Problem] --> B[Use-case Discovery]
B --> C[Data Readiness]
C --> D[Architecture & Roadmap]
D --> E[Build: AI / Data Product / Dashboard]
E --> F[Governance, Risk & Evaluation]
F --> G[Adoption & Change Management]
G --> H[Measured Business Impact]
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ML-powered income prediction using demographic and socio-economic data. Leadership lens: Shows how data can support classification, segmentation, and decision intelligence. Tech: Python, ML, preprocessing, model evaluation |
A Streamlit recommendation app for discovering movies based on user preferences. Leadership lens: Demonstrates product thinking around recommendations, UX, and analytics-driven personalization. Tech: Python, recommender systems, Streamlit |
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Plant disease diagnosis from leaf images using computer vision. Leadership lens: Applies AI to real-world diagnosis and field decision support. Tech: Computer vision, image classification, ML deployment mindset |
End-to-end housing price prediction project using ML and an interactive app. Leadership lens: Connects predictive modeling with real-estate business decisions and dashboard-style consumption. Tech: Python, Random Forest, Streamlit, analytics |
